Improving Bus Transit Safety Through Rewards and Discipline
Bibliographic record
Abstract
This synthesis addresses the current practices and experiences of public transit agencies in applying both corrective actions and rewards to recognize, motivate, and reinforce a safety culture within their organizations. The synthesis may be used to aid public transit agencies and other stakeholders in deciding how to proceed in this area. A literature review summarizes reports and documents, addressing the connection between employee safety performance and reward programs, as well as the effectiveness of reward/discipline initiatives in transit organizations. The survey of selected transit agencies yielded an 83% response rate, 25 of 30. Follow-up telephone interviews held across the country included a range of small to large transit agencies, rural and urban, and multimodal systems and addressed such issues as organizational commitment to safety, engagement of the work force, labor partnerships, safety standards and practices, rewards and discipline, and operations and maintenance. Nine case studies offer additional insight on active and innovative practices and related issues on the use of reward and discipline programs to promote and improve bus transit safety. Case study agencies were: Dallas Area Rapid Transit (Texas); Fayetteville Area System of Transit (North Carolina); GO Transit (Ontario, Canada); King County Metro (Seattle, Washington); Minnesota Valley Transit Authority (Twin Cities, Minnesota); River Cities Public Transit (Pierre, South Dakota); SouthWest Transit (Eden Prairie, Minnesota); Utah Transit Authority (Salt Lake City, Utah); and Wind River Transportation Authority (Riverton, Wyoming).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".